NDReg: Linear Registration

The algorithm will perform well when the reference and input images are linear transformations of each other. It will perform poorly if the reference and input images are non-linear warps of each other.

We will evaluate the performance of the algorithm on two real datasets: BNU1 and BNU2. The BNU1 dataset consists of 46 subjects, each of which have two scan sessions. This gives us a total of 92 brain samples to work with, each of which are 4D scans: 91 x 109 x 91 brain at 200 timesteps. The BNU2 dataset is the same except there are 61 subjects with 2 sessions each, giving us 122 samples in total. A sample brain from both datasets is shown below.

I expect the algorithm to perform well when registering the BNU1 dataset to the MNI152 template because the overall shape of the input and reference images are linear transformations of each other.

Simulations

Below is the code used to generate simulated data and run the algorithm code on said data. We will perform two types of simulations: good and bad. The good simulations will consist of inputs and references that are linear transformations of each other, so the algorithm will perform well. The bad simulations will consists of inputs and references that are non-linear transformations of each other, so the algorithm will perform poorly.

For the good simulations, the input will be a cuboid and the reference will be a linearly transformed cuboid. For the bad simulations, the input will be a cuboid and the reference will be a deformed circle.

We will qualitatively assess the results of the simulations by overlaying the reference and the registered input image, and checking how well they match up. We will quantitatively assess the results of the simulations by taking the MSE between the reference and the registered input image.

Code and results below:

%matplotlib inline

from ndreg import * import math import numpy as np import SimpleITK as itk

for lolololol in range(1): n = 200 a = np.random.randint(low=50, high=150, size=1, dtype=np.int16)[0] b = np.random.randint(low=50, high=150, size=1, dtype=np.int16)[0] c = np.random.randint(low=50, high=150, size=1, dtype=np.int16)[0] d = np.random.randint(low=50, high=150, size=1, dtype=np.int16)[0] r = np.random.randint(low=20, high=50, size=1, dtype=np.int16)[0] y,x = np.ogrid[-a:n-a, -b:n-b] z,w = np.ogrid[-c:n-c, -d:n-d] mask = xx + yy <= rr mask2 = ww + zz <= rr

checkers = np.ones((n,n))
for i in range(n):
    for j in range(n):
        if (i % (n/5) == 0) and (j % (n/5) == 0):
            checkers[i:i+(n/10), j:j+(n/10)] = 0.5
        if (i % (n/5) == (n/10)) and (j % (n/5) == (n/10)):
            checkers[i:i+(n/10), j:j+(n/10)] = 0.5

#inData = np.zeros((n,n))
inData = [[1,1,1],[1,1,1],[1,1,1]]
#inData[mask] = checkers[mask]
inImg = itk.GetImageFromArray(inData)
#refData = np.zeros((n,n))
#refData[0:50,0:50] = 1
refData = [[0,0,0],[1,1,1],[1,1,1]]
#refData[mask2] = checkers[mask]
refImg = itk.GetImageFromArray(refData)



(field, invField) = imgMetamorphosis(inImg, refImg, iterations=2, verbose=True)
defInImg = imgApplyField(inImg, field, size=refImg.GetSize())

imgShow(inImg)
imgShow(refImg)
imgShow(defInImg)
plt.imshow(itk.GetArrayFromImage(refImg - defInImg))

%matplotlib inline

from ndreg import *
import math
import numpy as np
import SimpleITK as itk

preErrors = []
postErrors = []

for iters in range(10):
    n = 50
    a = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    b = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    c = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    d = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    e = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    f = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    r = np.random.randint(low=5, high=15, size=1, dtype=np.int16)[0]
    r2 = np.random.randint(low=5, high=15, size=1, dtype=np.int16)[0]

    inData = np.zeros((n,n,n))
    inData[(a-r):(a+r), (b-r):(b+r), (c-r):(c+r)] = 1
    inImg = itk.GetImageFromArray(inData)
    refData = np.zeros((n,n,n))
    refData[(d-r2):(d+r2), (e-r2):(e+r2), (f-r2):(f+r2)] = 1
    refImg = itk.GetImageFromArray(refData)
    

    affine = imgAffineComposite(inImg, refImg, iterations=100)
    defInImg = imgApplyAffine(inImg, affine, size=refImg.GetSize())
    
    print("sim " + str(iters))
    imgShow(inImg, numSlices=1)
    imgShow(refImg, numSlices=1)
    imgShow(defInImg, numSlices=1)
    imgShow(refImg - defInImg, numSlices=1)
    preError = imgMSE(inImg, refImg)
    postError = imgMSE(defInImg, refImg)
    print(preError)
    print(postError)
    print("")
    print("")
    preErrors.append(preError)
    postErrors.append(postError)
    

sim 0

0.060608
0.00445146816948


sim 1

0.029952
0.00809657603613


sim 2

0.039104
0.0016489510704


sim 3

0.12896
0.0147846966559


sim 4

0.120224
0.00551982604428


sim 5

0.047744
0.0172604853029


sim 6

0.148448
0.0354477407239


sim 7

0.171392
0.0215591487415


sim 8

0.074688
0.0194869136086


sim 9

0.06992
0.00281795904415

%matplotlib inline

from ndreg import *
import math
import numpy as np
import SimpleITK as itk

preErrors2 = []
postErrors2 = []

for iters in range(10):
    n = 50
    a = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    b = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    c = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    d = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    e = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    f = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    g = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    h = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    i = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    j = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    k = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    l = np.random.randint(low=15, high=35, size=1, dtype=np.int16)[0]
    r = np.random.randint(low=5, high=15, size=1, dtype=np.int16)[0]

    z,y,x = np.ogrid[-d:n-d, -e:n-e, -f:n-f]
    mask = x*x + y*y + z*z <= r*r
    z,y,x = np.ogrid[-g:n-g, -h:n-h, -i:n-i]
    mask2 = x*x + y*y + z*z <= r*r
    z,y,x = np.ogrid[-j:n-j, -k:n-k, -l:n-l]
    mask3 = x*x + y*y + z*z <= r*r
    
    inData = np.zeros((n,n,n))
    inData[(a-r):(a+r), (b-r):(b+r), (c-r):(c+r)] = 1
    inImg = itk.GetImageFromArray(inData)
    refData = np.zeros((n,n,n))
    refData[mask] = 1
    refData[mask2] = 1
    refData[mask3] = 1
    refImg = itk.GetImageFromArray(refData)
    

    affine = imgAffineComposite(inImg, refImg, iterations=100)
    defInImg = imgApplyAffine(inImg, affine, size=refImg.GetSize())
    
    print("sim " + str(iters))
    imgShow(inImg, numSlices=1)
    imgShow(refImg, numSlices=1)
    imgShow(defInImg, numSlices=1)
    imgShow(refImg - defInImg, numSlices=1)
    preError = imgMSE(inImg, refImg)
    postError = imgMSE(defInImg, refImg)
    print(preError)
    print(postError)
    print("")
    print("")
    preErrors2.append(preError)
    postErrors2.append(postError)
    

sim 0

0.100416
0.0248697411851


sim 1

0.078368
0.033651488166


sim 2

0.047416
0.0166556154498


sim 3

0.046984
0.0263910743591


sim 4

0.103496
0.0317249339448


sim 5

0.095912
0.0189975426194


sim 6

0.151936
0.0253941900853


sim 7

0.076528
0.024669743711


sim 8

0.159896
0.0571209795871


sim 9

0.04892
0.0163580499486

We will quantitatively assess the population results by averaging the MSE for both types of simulations. We will qualitatively assess the population results by plotting the MSE from each simulation.

print("Good simulation pre-registration MSE:" + str(np.mean(preErrors)))
print("Good simulation post-registration MSE:" + str(np.mean(postErrors)))

print("Bad simulation pre-registration MSE:" + str(np.mean(preErrors2)))
print("Bad simulation post-registration MSE:" + str(np.mean(postErrors2)))

Good simulation pre-registration MSE:0.089104
Good simulation post-registration MSE:0.0131073765397
Bad simulation pre-registration MSE:0.0909872
Bad simulation post-registration MSE:0.0275833359056

x = range(10)
plt.scatter(x, postErrors, c='b', s=40, label='Good simulations')
plt.scatter(x, postErrors2, c='r', s=40, label='Bad simulations')
plt.legend(loc='upper left')
plt.xlabel("Simulation number")
plt.ylabel("MSE")
plt.title('Post-Registration MSE')

<matplotlib.text.Text at 0x7f8f4a8e7790>

We can see from the above results that our algorithm performed as expected on the simulations. The MSE metric is simply a sanity check and we can see that registration decreased the MSE for all simulations. We can also see from the plot that the good simulations performed slightly better than the bad simulations, overall. Now, we move on to the real data.

Real Data

import nibabel as nb

for subject in range(864,910):
    for session in range(1,3):
        print(str(subject) + " " + str(session))
        filename = "BNU_1_0025" + str(subject) + "_" + str(session) + "_rest.nii.gz"
        brain = nb.load("../resampledBNU1/" + filename).get_data()
        brainslice = brain[:,:,:,0]
        inImg = itk.GetImageFromArray(brainslice)
        reference = nb.load("MNI152_T1_2mm_brain.nii.gz").get_data()
        refImg = itk.GetImageFromArray(reference)
        
        affine = imgAffineComposite(inImg, refImg, iterations=100)
        defInImg = imgApplyAffine(inImg, affine, size=refImg.GetSize())
        
        newbrain = np.zeros(brain.shape)
        newbrain[:,:,:,0] = itk.GetArrayFromImage(defInImg)
        
        for step in range(1, brain.shape[3]):
            brainslice = brain[:,:,:,step]
            imgslice = itk.GetImageFromArray(brainslice)
            regslice = imgApplyAffine(imgslice, affine, size=refImg.GetSize())
            newbrain[:,:,:,step] = itk.GetArrayFromImage(regslice)
            
            
        identity = np.diag([1,1,1,1])
        newbrainimg = nb.Nifti1Image(newbrain, affine=identity)
        nb.save(newbrainimg, "affinedBNU1/" + filename)
            
        
        print(affine)
        imgShow(inImg, numSlices=1)
        imgShow(refImg, numSlices=1)
        imgShow(defInImg, numSlices=1)
        imgShow(refImg - defInImg, numSlices=1)

hello
864 1
[0.7445501461363982, 0.2603578696262745, 0.001100458409852234, -0.055028137199373904, 0.8173067143557746, 0.11905071376231097, 0.07664471793645466, 0.006724044115866239, 0.9267729141404412, -7.583848417355384, -4.825593976002003, 4.159374977853747]

864 2
[0.7361454365716074, 0.2504194003600531, -0.0011590029732160556, -0.06193308228164219, 0.8223020798982489, 0.118661322733825, 0.08583636328054017, 0.0004055711203889839, 0.9282743729415049, -7.5883938321134865, -5.206971157813126, 3.760321293278242]

865 1
[0.7307722843439544, 0.16898529894727624, 0.10073717235807146, -0.0007929158630379444, 0.9076535503102858, 0.008348279494285965, 0.03147602689782974, 0.06844310227668692, 0.8764696322308984, -6.465924439279815, -5.25925995131586, 5.38198697874151]

865 2
[0.7479126877841604, 0.15393239763675248, 0.1192086248888137, 0.007042090533083457, 0.911618367152068, 0.02043936980323865, 0.029584369584460878, 0.05240767735857221, 0.8777337038796383, -5.899738183268873, -4.391131292112398, 5.606872286652087]

866 1
[0.6683940392459482, 0.2370564436148815, 0.13597510535435015, -0.09915367202323339, 0.7518529074265257, 0.22777353979568, 0.12077970335131175, -0.09693773728500084, 0.9259968956286334, -6.804411744084699, -5.927529673782524, 3.93574369522719]

866 2
[0.6634930029590632, 0.26777057681842814, 0.11296880168640325, -0.11032762015465493, 0.7392979179316237, 0.24933885952142684, 0.13150678203441257, -0.10968794531074111, 0.9545158131471376, -5.769828994791216, -5.6250224189774185, 3.7950979804267235]

867 1
[0.7397503463564306, 0.20458293329962227, 0.04185876195426788, -0.04183296395927204, 0.8152845987373141, 0.09867196104880624, 0.07196714701637805, -0.03135647559827067, 0.9631594117530847, -6.426300508621167, -5.837425772987669, 4.92426547447898]

867 2
[0.7329269067393244, 0.19368471803770632, 0.05114616601366098, -0.0323343340178723, 0.8292483390358013, 0.07705915919110327, 0.033045811130588965, -0.004252182893542199, 0.9724671358275951, -6.758439842677692, -6.18102080468982, 3.963910713194563]

868 1
[0.7234804091555962, 0.19489302282786852, 0.06396233055265263, -0.0860191674151025, 0.8276525513178041, 0.13627363508287188, 0.08514252655745119, -0.033073803483470686, 0.9487819246422775, -5.219722315278996, -7.332357299522732, 4.198445894192669]

868 2
[0.7275064630570983, 0.17902427217181768, 0.09106891977580858, -0.0625140876118146, 0.8413520642460671, 0.12704605038516179, 0.05743285547152902, -0.018348211434376913, 0.9503538991459919, -7.499232877369413, -4.513259527485448, 4.517009121329607]

869 1
[0.7726632867815086, 0.18256664985257526, 0.09314169049013207, 0.13816370339512968, 0.637977344570183, 0.09385383320640815, 0.01349333968297923, 0.03082571540178764, 0.925493059863183, -7.876011480311169, -3.763081757518612, 4.7513098926582265]

869 2
[0.7695543435390824, 0.19149746945345325, 0.07821517570298252, 0.14235940617051, 0.6245068036063571, 0.09252133384954, 0.008969891626270352, 0.04377397655469495, 0.9227525148264609, -8.022501183839685, -3.8963040740136052, 4.402780967890111]

870 1
[0.7563536951299307, 0.18539948255380145, 0.06325270875585971, -0.0033140211609037104, 0.8354586583185962, 0.10480737771171626, 0.04735183443220481, -0.05313569525376709, 0.9847817243512377, -6.84559922334776, -5.49984566336201, 4.357059465047362]

870 2
[0.7390810569536106, 0.190465625405389, 0.060409933345932434, 0.0021463849797594987, 0.8352243060300407, 0.08005428190413438, 0.02817024881813555, -0.03416817574066185, 0.9938033982906107, -6.182503604532049, -5.419300722939339, 5.5092667564032185]

871 1
[0.7670244691318874, 0.19161516541368576, 0.03231508885842571, -0.012672627603176164, 0.8740722330678772, 0.01897924410345628, 0.08356941734877782, 0.005477428910191814, 0.9024178628031598, -7.2924060667547055, -6.072157598897192, 3.066064456145557]

871 2
[0.7669653707943712, 0.21547442665440972, 0.024963030543565412, -0.021134256834206732, 0.8693185794768545, 0.04726310706045688, 0.09346016721555508, -0.01215550486346708, 0.9111323842131944, -7.084871406985838, -5.126811089276957, 4.717963008723721]

872 1
[0.6498351968063102, 0.3034317730071998, 0.02366028749592075, -0.10877581271920982, 0.815105901377, 0.1344987084101008, 0.1762950034014642, -0.08670378560873446, 0.9286108922174039, -6.098425818011603, -5.635711163385533, 5.51614877498632]

872 2
[0.6666005868113734, 0.2851533317982537, 0.03571943683408266, -0.08192782987083422, 0.8305944833405858, 0.11402047844114115, 0.14060959683206217, -0.06489319921794914, 0.9327081959904566, -6.702633535843101, -4.617165897497423, 5.697347213501119]

873 1
[0.791803957453707, 0.1844531762725788, 0.012799364294850998, -0.029180834143445522, 0.8137237966378836, 0.11949132093291115, 0.12716510744673717, 0.006475910420969601, 0.8831395645516378, -8.218139989088534, -3.716123651566504, 4.159374384561039]

873 2
[0.8011598602825318, 0.14744714113496254, 0.0527864984060757, 0.018773865716751683, 0.8035198593548284, 0.10272846036711851, 0.09610050532628919, 0.02796566621136092, 0.8833500255704987, -8.712075028204932, -2.879567511726531, 3.869978791962764]

874 1
[0.7252749202396187, 0.1705010894153022, 0.08461411930483427, -0.05677748965862903, 0.7787471598163428, 0.14568982617762094, 0.07806605026034857, -0.016385323729377583, 0.9309150438776651, -5.992093947007646, -7.0198732732154, 3.6997564191164316]

874 2
[0.749108793478732, 0.12402278699699891, 0.14887355698742621, 0.006749526602250999, 0.7939782152334842, 0.12393146543293622, 0.024222137677923816, 0.01655799897640489, 0.9355450254220117, -7.464021559656444, -4.329976559358436, 4.7705957587302725]

875 1
[0.7382903601611617, 0.18014467349672256, 0.06345512261348085, 0.034549008791809145, 0.7903749177418938, 0.019798240056534078, 0.05852075911779731, 0.041194201945694404, 0.8938892017126198, -6.356246490683052, -6.4931763668341125, 4.074172154158771]

875 2
[0.7455762159548973, 0.18268499889287548, 0.1210406174436027, 0.09973333940666441, 0.7564818893977069, 0.02038472252804665, 0.028328480565341166, 0.05130559259074073, 0.8950064488777107, -6.566000013937386, -5.203778142826537, 5.266012131733417]

876 1
[0.6927308557616194, 0.22625166876852496, 0.07393076080175078, -0.07052242247531179, 0.844914080276917, 0.14501357179239804, 0.046206831681580844, -0.06745186607648448, 1.006731653328895, -5.683102409944931, -5.37118179091932, 5.898277895860199]

876 2
[0.6941490555590164, 0.22733721020329786, 0.06816167766781177, -0.06155496960237617, 0.8489453416322236, 0.13289655080610852, 0.03203660203864571, -0.05604512504558341, 1.0088169545112036, -5.426713067482184, -5.4321151756255315, 5.7369369702948685]

877 1
[0.7943679363139603, 0.14075411213608774, 0.05187605150889171, -0.02222197512276014, 0.86610191858977, 0.06964108033608951, 0.031149466118894867, 0.0023707277537857088, 0.9630110454292481, -7.664594584649321, -4.7056577402383635, 4.31190561662167]

877 2
[0.8086278946295151, 0.14938328575766038, 0.0475057519276562, -0.019692900778906734, 0.8607960631837183, 0.0749938007553821, 0.03707166012197051, -0.004052630310901945, 0.9602884138282629, -7.650957641442068, -5.004104995088738, 3.9467705230143837]

878 1
[0.8152338649692078, 0.1202069597117413, 0.05119559750004307, -0.03260949756866532, 0.8731018235012011, 0.06149798466942879, 0.048528625543983045, -0.020296891377570937, 0.9775978758296607, -7.347311821004746, -4.410658245160153, 5.100455720572747]

878 2
[0.8261471452361392, 0.11393108271441464, 0.03141922631999372, -0.00997653299904927, 0.8647238236776981, 0.03176314299778237, 0.03765644467081961, 0.0043420569030322015, 0.9773262199282289, -7.32119712768542, -4.167573225729513, 5.3441873726224225]

879 1
[0.7432024894611868, 0.1949839576688427, 0.05533884199338064, -0.024075614194784652, 0.8447103567848684, 0.09121606766279564, 0.04873831593759598, -0.04217291764880804, 0.9859228116694863, -6.403713315489632, -4.573160795439677, 6.029427851518504]

879 2
[0.7480446652622869, 0.1911920138788021, 0.059108736541516035, -0.023430842506795106, 0.8497597247009846, 0.09466968869618766, 0.052056354374410724, -0.044193993416771625, 0.9858022064545606, -6.719289405503673, -4.8736628032744, 5.372340256304942]

880 1
[0.7206333598296334, 0.22310036529602625, 0.06157908573677871, -0.03093226009439445, 0.812530637977431, 0.1041941959889404, 0.07320213842820211, 0.00022798769650451967, 0.9225588828963437, -6.464718323651516, -4.444489739726597, 5.980320248520621]

880 2
[0.7312094650586203, 0.23733185440204035, 0.05008770643539846, -0.037068360982479696, 0.8093952009421083, 0.12438484524282252, 0.08247589967434, -0.00668921534435632, 0.9210187294727974, -6.968160767375283, -4.586650190636033, 5.18634649165477]

881 1
[0.7970694342919274, 0.14910764750075367, 0.08787873863632469, 0.058476608179821706, 0.7642178536418237, 0.06691041717975695, 0.03202070286774603, -0.0022113906170085065, 0.9566197772475536, -8.246300586148617, -3.57661146587969, 4.3103772221307155]

881 2
[0.793961173425348, 0.13047809208851396, 0.0809147577356242, 0.018610821973233348, 0.8321054276028567, 0.04966313277404458, 0.013310044478486799, 0.02680755915174135, 0.9563693223833292, -6.922674316100467, -5.268996721800025, 4.864527959355374]

882 1
[0.7355526253345372, 0.13237863759682406, 0.1619939413389178, 0.04769245540259563, 0.7932745881712681, 0.09601482110869825, 0.04338829983514127, 0.0854050152664832, 0.8341545275893412, -7.100990459287061, -4.601054715011326, 5.156954387007389]

882 2
[0.7298174091763475, 0.14619226883171832, 0.1479603850863665, 0.02573827202120146, 0.7953429990876689, 0.12192525004877604, 0.07113927994524137, 0.045707467088360564, 0.8444262633211717, -8.408549207147214, -2.9525002303158994, 4.401888168332507]

883 1
[0.8235666269699564, 0.1040738244737387, 0.0752724219658905, 0.010751539227704205, 0.8846476110855106, 0.04270114152282919, 0.014836283489791099, 0.012121706113650831, 0.9577461321790748, -7.77555424881905, -3.7496755914152553, 4.953946774419517]

883 2
[0.8150164691435744, 0.1379463193735821, 0.052450899109447495, -0.012842307494081221, 0.8773028185966649, 0.06541102892549738, 0.03930188530613786, -0.004124588346442113, 0.9568665356424907, -8.533313111890836, -3.990503515285543, 3.175241832819746]

884 1
[0.7636068424253163, 0.19306182653555906, 0.007638439610095041, -0.024780502953588544, 0.8159926544874687, 0.09880325265750967, 0.0926335829397901, -0.05553088431779869, 0.9665211966579076, -7.6901642616100725, -4.660095411532429, 4.318028186732268]

884 2
[0.7659103140753187, 0.19100241399634169, -0.0010020707487176789, -0.023178714199290167, 0.8151540488985286, 0.09868959857257606, 0.09898546231433189, -0.05777343464311075, 0.9700435520619329, -7.847069111016303, -4.010234345312589, 4.689193064027517]

885 1
[0.7740944876940972, 0.10716114512008755, 0.1350453362526567, 0.05384296900133097, 0.7784205251833688, 0.06720659140625831, 0.06398879751490866, 0.015884378506710106, 0.8995913859995689, -7.493520077288508, -4.007863567605558, 5.152440513511969]

885 2
[0.7771864932474059, 0.0815987011819052, 0.14500951199524062, 0.03880300502321848, 0.8174931905865457, 0.059638332152729255, 0.06390423191964047, 0.02776736419014686, 0.8915576444848395, -7.8347585160674225, -4.4221635736304075, 4.247212527195698]

886 1
[0.786388755167253, 0.13952750425601146, 0.05842336752924186, -1.244147694622348e-05, 0.8182612143826609, 0.07245100860447863, 0.0804093458499785, 0.0070220438948005795, 0.9155793619636977, -6.934449277471922, -4.55635796857129, 5.504496081288624]

886 2
[0.7939245624534533, 0.13188595797304495, 0.09282511222287501, 0.0252939603251438, 0.8091715315208274, 0.08843575802130998, 0.08137708164995681, -0.002903678739594023, 0.9148367180393949, -7.149667174967191, -4.623636210028476, 5.0945579739019]

887 1
[0.7670052109692492, 0.13054519943777296, 0.10935708170323392, -0.005974286304549362, 0.8624760971334408, 0.08840303822409, 0.008434400678861928, 0.0007815514565431489, 0.9693342084215767, -6.886579719772058, -5.419447875917908, 4.666387738650454]

887 2
[0.7758464235978645, 0.1190921331667866, 0.09362016550445096, -0.011814933575749354, 0.8628519201207511, 0.0835073255524281, 0.022481189605862692, -0.006916223053344085, 0.9734139888756875, -6.7499463031998, -5.531906576135513, 4.8131374136267135]

888 1
[0.7116942307232043, 0.20092197811869475, 0.07562202055646722, -0.0828184355002877, 0.7999272283213607, 0.16833926230094262, 0.06877362772567833, -0.04242413049314738, 0.979577445475073, -5.481045986001454, -6.00127275505617, 5.694184825925311]

888 2
[0.7263314239510787, 0.190820254718895, 0.10737413932688009, -0.06979873028726907, 0.8015882597859919, 0.17741647666547178, 0.04871557281546364, -0.04016895030330864, 0.9808731562842519, -5.1461825191329345, -5.928824131014019, 3.954133951013856]

889 1
[0.7134356748151253, 0.18371340061529132, 0.10406253837291914, -0.0023963169441486593, 0.7455107349859699, 0.11987042445493357, 0.09574068640483377, -0.04786360051162434, 0.9180326485653051, -7.508448128680788, -4.621328119454556, 4.562941078263145]

889 2
[0.7014032095064737, 0.23730469934675516, 0.07321034180429449, 0.011582488153679515, 0.6921587431288716, 0.12074084378743724, 0.10093724558116486, -0.036433725610556904, 0.9235797051434433, -6.021500029869308, -4.656586324782148, 6.27906553933025]

890 1
[0.699811453859843, 0.1762805587441231, 0.14816438677183055, -0.003482836326857538, 0.8118729281003878, 0.09767802593881257, 0.05426442849953897, -0.019827679154065393, 0.928038757825379, -5.7215851919959055, -4.272873224053278, 5.459142523570667]

890 2
[0.6960994773473362, 0.18371692459966385, 0.1276482913672709, -0.014467328372814073, 0.8270260293013105, 0.0996227012320044, 0.049265234206952294, -0.004677059475557084, 0.923319961107657, -5.295891262671695, -4.7171428168626095, 5.830100590882911]

891 1
[0.7706134894983558, 0.12397054500118214, 0.09742936329005199, 0.016755360689463414, 0.8499460225424986, 0.07145857898675316, 0.08413473951265968, 0.019768334450699095, 0.8774751840592631, -7.957216682526673, -5.055777246883274, 3.1823828343405407]

891 2
[0.7654860272050139, 0.0858656603731315, 0.14217867185797836, 0.05888788106716358, 0.8565300238517408, 0.044003224786347564, 0.06098074361225612, 0.04183423978442048, 0.8769157094410398, -8.053771851343445, -3.0130597054824837, 5.0319283569643565]

892 1
[0.7432821456949712, 0.19239517197438646, 0.07431169722782305, -0.003359229797734036, 0.7241719801360278, 0.1371112364758696, 0.11619629873543802, -0.006713762912898626, 0.8696758502840047, -7.669238901631451, -4.077331153752255, 4.803502640584244]

892 2
[0.7435554684664384, 0.21387487691758233, 0.08187039761548304, 7.451886652722626e-05, 0.7133864759321762, 0.14337668076811427, 0.07512947325870217, 0.02705978490244856, 0.8835131961671603, -7.476011762033941, -4.444457930404214, 4.596453596849981]

893 1
[0.742290470492295, 0.1916999627243975, 0.12533412342189154, 0.06895171166594115, 0.7205757224146286, 0.05402665223752971, 0.013805027508082997, 0.04905625722515917, 0.896116595731695, -7.446347374603404, -5.152247233487673, 4.102001584506204]

893 2
[0.7469274037199413, 0.19692789870310068, 0.10285492995690199, 0.06373168568085641, 0.7240293417047176, 0.05730069374618335, 0.017913482255685467, 0.055905378207087304, 0.900372725429625, -7.793420047106909, -3.2881578019562605, 5.232871106763912]

894 1
[0.7962992267710207, 0.1356249131196047, 0.07538261602528634, 0.05421811341117858, 0.7989176817005393, 0.0779358654859483, 0.09324958654059289, 0.004536627669517065, 0.896450062529559, -7.86765531786631, -5.593148651393622, 2.4202292174714297]

894 2
[0.8117024332385888, 0.11611448398295782, 0.06716953736153743, 0.061146597891489754, 0.7952864016468999, 0.06572512136307679, 0.08086329072882774, 0.03237207021163238, 0.8953051093011907, -7.751780609907968, -4.424529599043877, 4.341984683803636]

895 1
[0.7126352912213948, 0.23094633290651598, 0.10676068010229035, 0.06727142133835155, 0.696467443049012, 0.0769158021987007, 0.09407743108549071, -0.028237056874345495, 0.9055787664559253, -6.157744917426138, -4.891902002839047, 3.4533882588379172]

895 2
[0.6990905564740616, 0.23283434301762895, 0.08675909887849714, 0.018926952285741185, 0.7465503535037937, 0.09250902799657804, 0.10520121850551659, -0.03668179612964194, 0.9189508096101674, -7.1693409482853045, -3.1198542000357947, 5.813250413252924]

896 1
[0.7240924079558433, 0.2382763870072688, 0.05809609273071706, -0.041657824033486106, 0.7855640072037114, 0.125496159444118, 0.10857207655687688, -0.05386830504367986, 0.9215045715814054, -7.991230305452072, -4.220343595980653, 3.92852437275994]

896 2
[0.740907079016981, 0.20408314848127257, 0.05158974765213181, -0.011567410591334967, 0.797046570791027, 0.0861784298787699, 0.07061635670725, -0.017342333005294547, 0.9306942346821317, -7.629651773680089, -4.266472868214552, 4.720112843189292]

897 1
[0.7457655170192702, 0.16829249396242804, 0.11315714812171032, 0.007899388894997774, 0.7319570871640149, 0.16632178930464822, 0.09485530470567821, -0.006328588338993682, 0.8670376989666904, -8.238118775586534, -3.7118565606040277, 4.024842242925993]

897 2
[0.7421628494117737, 0.17791707972382434, 0.08785335337248246, -0.011080549332294598, 0.7414331204819038, 0.15750004748714297, 0.09323428496551728, 0.0033654499687760624, 0.8831228455190547, -7.260991947698049, -4.967551692365544, 4.544543125445746]

898 1
[0.7845009293936138, 0.11568464239255662, 0.09752401551427563, 0.03973503093298529, 0.8254661981472405, 0.03959258743784959, 0.0796508496324482, 0.0024798857662379787, 0.8856071588308004, -6.828354101154326, -6.0997775501343785, 3.9511771702883176]

898 2
[0.7656303803504836, 0.16929576275212294, 0.06952728656757112, 0.0038662368553981034, 0.8309980965707529, 0.06725058603026407, 0.1106057360169683, -0.017416496196226733, 0.8842212026608179, -7.669538410928518, -4.161737809774176, 4.755900516284567]

899 1
[0.7597515084654581, 0.23379569783753243, -0.029687956981676755, -0.08843120264826863, 0.8614366915037962, 0.12311055426795517, 0.14243374321806657, -0.06329358904740344, 0.9395592313878298, -6.540547949375146, -4.760228021338223, 5.816850930376355]

899 2
[0.75726566205265, 0.20018701051423704, 0.04834693909845311, -0.04533471805570742, 0.8823863093712013, 0.08189503868048345, 0.0735516790132463, -0.025330822471328424, 0.9399324060840607, -6.8633849727722245, -3.911466093144164, 5.82885473057386]

900 1
[0.7637810516173247, 0.14498429812662658, 0.09792077818365516, 0.03482881482447858, 0.7604036080607003, 0.1251627837662631, 0.10516356284625988, -0.035398894943614964, 0.9113553526085719, -7.073887273749711, -4.77551676703285, 5.0360741078451445]

900 2
[0.757699610307626, 0.14518143689379126, 0.10917203302622869, 0.045033112922674264, 0.7577916362618695, 0.11280441822849528, 0.09789023725455957, -0.02649153825976047, 0.9073347080611631, -5.759775927609325, -5.300777428721089, 6.063637276530246]

901 1
[0.7385937694008211, 0.16814106899419382, 0.09943363685551043, 0.08222405856852222, 0.7758503211327308, 0.0569153467330218, 0.13370797859073408, 0.03727275712445832, 0.8180852664699073, -7.822959407750328, -4.8625927433438925, 3.83104502989342]

901 2
[0.7375104443838196, 0.17385321266974374, 0.11187714440825794, 0.10346638211977141, 0.7432866722738293, 0.05632349137008617, 0.11807573194858897, 0.055197686178343044, 0.8251877676144623, -7.776218853284159, -4.330433651500742, 4.519488798835309]

902 1
[0.7775113318103924, 0.1478743440758455, 0.07412356616237334, 0.055922604879077915, 0.7863176436934982, 0.0489969918278531, 0.06092909607310572, 0.04049028329439439, 0.8849371166967378, -7.424152686799763, -4.854081532156183, 4.5281761390342785]

902 2
[0.7730912784033311, 0.17806199498658432, 0.037590072606646124, 0.02351412893381798, 0.7795774372665508, 0.08229057742204506, 0.09990207955645476, 0.013864270945837804, 0.885709002918671, -7.758521659676543, -4.101081319944631, 4.697388272165038]

903 1
[0.7542401054799639, 0.15376069240532445, 0.11110015770910361, 0.03191770203030201, 0.8031157216677101, 0.06860791865702372, 0.06503060618553275, -0.03862870946917642, 0.9449087659750115, -6.279560508037793, -4.707434135117102, 6.046047239499172]

903 2
[0.7402907839544571, 0.15386320889525976, 0.12009213782234676, 0.047268134069795714, 0.7783567965951155, 0.05362364245453564, 0.06133650910008009, -0.03386566115779583, 0.9531289673889324, -5.734986542341904, -5.990827637453661, 5.51865542897414]

904 1
[0.7200124162286499, 0.17828921300734513, 0.09519764146832577, -0.019460832892157876, 0.8062626021629037, 0.10745744959239883, 0.06798179056523733, 0.0036750450598799222, 0.9086170957128932, -7.539358538201047, -3.7312898126364247, 5.2547792007361025]

904 2
[0.7221407433849744, 0.2100071896770319, 0.08184516642601244, -0.05502599396108641, 0.8106600173351002, 0.1461296409382811, 0.09588333536649744, -0.021504036968935967, 0.9115621312959277, -6.125521989033503, -4.180840867656225, 6.221912770589614]

905 1
[0.7655485789013746, 0.22429503020778563, 0.01829738168262615, -0.012323801851010133, 0.7610226907475771, 0.12309887580569884, 0.09429003247143446, -0.007647063862372412, 0.9183554698225623, -8.203086281077493, -4.212982192123086, 3.600724222733055]

905 2
[0.7675768064996453, 0.22989916345308786, 0.015152382349619936, -0.020509430093100082, 0.764852615693751, 0.13598075490353517, 0.11188907432674737, -0.022901177309258593, 0.9142436248940087, -8.21138480762595, -4.464437523515955, 3.3113206514846754]

906 1
[0.7594740576059088, 0.17176052104600464, 0.08777575869555782, 0.03810705567044516, 0.8433370871698033, 0.04941066215198009, 0.06210676024378046, 0.03131547804169611, 0.8828341346632085, -7.761202258682654, -4.017272233903662, 4.748597573201458]

906 2
[0.7665893023385901, 0.1524854150368874, 0.08211614017922074, 0.03941123870810123, 0.8514048663875764, 0.05052197304806108, 0.06654174559717241, 0.01781531311156155, 0.895784445577621, -8.12104965144088, -3.8948579760514215, 4.255813267479584]

907 1
[0.7358044505031406, 0.15636765423762694, 0.12041455023850728, -0.023597642545248095, 0.8616189425349011, 0.1153127582035895, 0.02054679276021665, -0.006428159669156552, 0.9621609405349405, -5.251797568875573, -4.286900209493151, 5.183471476675675]

907 2
[0.7142084820153913, 0.21212523723589766, 0.08756545520229359, -0.05258804855106296, 0.8407911828527894, 0.13591304054255862, 0.047509562707644865, -0.0176380016849911, 0.956896819584283, -6.05163211349504, -5.037338991389429, 5.624192498741918]

908 1
[0.7097305497351848, 0.23518728267608846, 0.07193563630777818, -0.05901673399919376, 0.872455879254521, 0.08683402836519813, 0.058888490821269145, -0.039370594253982046, 0.9583594946599423, -6.712202217313388, -4.8120026801177795, 5.381906236936725]

908 2
[0.7172475165062338, 0.19758194593344294, 0.08099400056479125, -0.01605781496436641, 0.8724524917365221, 0.05065301615286153, 0.04997407630615803, -0.03572105678044669, 0.9641370793410586, -7.615924287169361, -4.161581378202969, 4.889433244212833]

909 1
[0.7922206548812791, 0.10258905768557883, 0.1255609846797197, 0.021170597915858368, 0.8107386818298105, 0.07480642395378935, 0.034577819491189765, 0.05278433308582503, 0.8788651381335487, -8.080232353365362, -3.456786478527425, 4.679552270944892]

909 2
[0.7810784180291513, 0.1318798989676042, 0.1070508956492915, 0.03797519488828101, 0.7732125069267122, 0.06315029497676604, 0.05164346192088354, 0.0456279610871369, 0.8782679868532279, -8.401858935873022, -2.596995436892745, 4.703809512600825]

import nibabel as nb

for subject in range(921,982):
    for session in range(1,3):
        print(str(subject) + " " + str(session))
        filename = "BNU_2_0025" + str(subject) + "_" + str(session) + "_rest.nii.gz"
        brain = nb.load("../resampledBNU2/" + filename).get_data()
        brainslice = brain[:,:,:,0]
        inImg = itk.GetImageFromArray(brainslice)
        reference = nb.load("MNI152_T1_2mm_brain.nii.gz").get_data()
        refImg = itk.GetImageFromArray(reference)
        
        affine = imgAffineComposite(inImg, refImg, iterations=100)
        defInImg = imgApplyAffine(inImg, affine, size=refImg.GetSize())
        
        newbrain = np.zeros(brain.shape)
        newbrain[:,:,:,0] = itk.GetArrayFromImage(defInImg)
        
        for step in range(1, brain.shape[3]):
            brainslice = brain[:,:,:,step]
            imgslice = itk.GetImageFromArray(brainslice)
            regslice = imgApplyAffine(imgslice, affine, size=refImg.GetSize())
            newbrain[:,:,:,step] = itk.GetArrayFromImage(regslice)
            
            
        identity = np.diag([1,1,1,1])
        newbrainimg = nb.Nifti1Image(newbrain, affine=identity)
        nb.save(newbrainimg, "affinedBNU2/" + filename)
            
        
        print(affine)
        imgShow(inImg, numSlices=1)
        imgShow(refImg, numSlices=1)
        imgShow(defInImg, numSlices=1)
        imgShow(refImg - defInImg, numSlices=1)

921 1
[0.7399209004490986, 0.05650823767006943, 0.08812736566649762, 0.06714771929848591, 0.8194896074786715, 0.02014972616427982, 0.08538297648813084, 0.013628879525495442, 0.9034745303264593, -9.159043573920131, -1.8694300103393995, 3.5052963373955004]

921 2
[0.7097254645594515, 0.041045016562005945, 0.12592475664024702, 0.07389434473774847, 0.8239352742247572, -0.011959413908830867, 0.05805718656102449, 0.05351036974112057, 0.8803007501168293, -9.0884032981881, -2.6660696682300435, 3.147089343223485]

922 1
[0.7133270675964893, 0.13230411020420818, 0.020552079563750464, 0.01952801672046111, 0.7821526444450323, 0.09069678207071463, 0.07353472545145383, 0.022116172086792993, 0.9390722713921614, -9.007607505502397, -2.2683557017914278, 3.6347169520443003]

922 2
[0.6996211195826356, 0.1372127565138653, 0.0338102549763107, 0.00860227487935362, 0.7789608709258038, 0.09758308462608177, 0.08265231675130671, 0.016248247132981418, 0.9318344324422128, -8.85361712414335, -2.6402800741907733, 3.7552062715446306]

923 1
[0.7267436251482722, 0.10504732036035636, 0.036162861061362475, 0.05287588647903911, 0.8125392318331717, 0.04243267545606254, 0.06589890627436291, 0.06191305817562044, 0.9076351856630703, -8.955682651677673, -1.344999960404877, 4.211766991561147]

923 2
[0.7075488606905606, 0.1393388582340629, 0.04079957039914975, 0.05281150676483246, 0.7435642774429754, 0.06998930148060008, 0.08373854454479779, 0.024981895674567326, 0.906614319207595, -9.176729402228181, -1.5931950478890913, 3.5709300451711]

924 1
[0.6851218611317419, 0.14920413712937666, 0.019133430683914238, 0.032279651260019596, 0.8374012870752244, -0.002345931639778813, 0.05424394044479963, 0.059899958017512696, 0.9101174936219882, -9.031944185312824, -2.49050439970977, 3.428173048390683]

924 2
[0.6901946912520118, 0.1468946359100213, 0.05679486100710278, 0.013836201069874729, 0.8514400746624957, 0.013987212415254927, 0.059233416120968996, 0.029359627971605948, 0.9165002918305872, -8.872182063237934, -3.1863178131831273, 3.242621274893289]

925 1
[0.6934677647569486, 0.1262748588614882, 0.019647940758631452, 0.029373091726070524, 0.7579893703224787, 0.08774716739655346, 0.09629140750291569, 0.007218974761281168, 0.9425066475207805, -9.046301347561103, -2.515425083813339, 3.354179705704556]

925 2
[0.660948794263946, 0.15473807199323142, 0.0038686321811661484, 0.020165975279319615, 0.7439383121285, 0.09919400549340511, 0.1253284074210411, -0.0012067512320152862, 0.9407200298117806, -8.886157490580949, -2.5719009608793777, 3.7161491500490262]

926 1
[0.7468776908880936, 0.07334194999109314, 0.04901031835838052, -0.008404329946693037, 0.9231011252324524, -0.02191930513467743, 0.07171467122346967, -0.004509444187826374, 0.9493433902709083, -9.298650408066493, -2.1694026845725753, 2.9167667564601096]

926 2
[0.6979619534394752, 0.12246837343366161, 0.026767925132748185, -0.05485054200158626, 0.9315230173235124, -0.03338274879911159, 0.082059416216004, 0.021778808635662304, 0.9209342480176836, -8.98153023540981, -3.0521345321519107, 3.102444637355544]

927 1
[0.702228525043601, 0.13580064431619612, 0.023504946660856896, -0.03128973814690114, 0.8362175471519873, 0.05841290127261131, 0.06153693904312023, -0.006308989781355186, 0.9813377400723649, -8.834295734213514, -3.1841554642363135, 3.363797123398777]

927 2
[0.68756831393247, 0.1674012225242864, 0.011097845190875255, -0.04987735997203571, 0.826584049887618, 0.08378459016441757, 0.09497648842290206, -0.031231157819827885, 0.9705941279786716, -8.792098551328849, -3.474330404185552, 3.1821121982493774]

928 1
[0.7563953808204338, 0.03524860405909534, 0.056851471171147194, 0.04330936859622971, 0.8981960165173939, -0.05156465961214213, 0.04978599963613738, 0.03296511884721362, 0.930337107144451, -9.41306574885964, -2.060161552851018, 2.6295945848581814]

928 2
[0.7665165785715944, 0.08191452119756877, 0.04285150708519665, 0.011841728905988462, 0.9086287767391216, -0.043391641273293514, 0.06462648201051957, 0.032399540466796654, 0.9280882891065292, -8.74489506458415, -2.886360161733312, 3.8499531874435404]

929 1
[0.7194351562364018, 0.0902768790723066, 0.05794913844944145, 0.00030660957218379384, 0.8643634412654585, 0.010196546921043652, 0.023756806000811096, 0.017436592948055232, 0.9777671884672795, -8.865412801833472, -3.0624007345882442, 3.4013855229861916]

929 2
[0.7048767623494854, 0.1340285951120635, 0.029481433472018134, -0.01843730665408967, 0.8452329450609091, 0.049356134582055636, 0.05758064624124469, -0.007535054299080524, 0.9729781523878287, -8.80534464443391, -2.658459990686997, 3.8688099060353913]

930 1
[0.7294278001455151, 0.14444624831958078, 0.015808161853867325, -0.04236056070098613, 0.8544460118366868, 0.058837420389866266, 0.05753637313389916, -0.003186835219690096, 0.9687287141493027, -8.859725535414672, -3.409714269319279, 3.0758340868016854]

930 2
[0.6978658385991979, 0.14214991507435085, 0.05811830452782779, -0.0037712103629493434, 0.8489916415327308, 0.0344396889333953, 0.03651727249845946, 0.022806278874593654, 0.9536770440945153, -8.815170576532747, -2.8974465632032635, 3.6802220798441536]

931 1
[0.6844126844033454, 0.20573451239163904, -0.007081795739964092, -0.003274481785222754, 0.746121221457358, 0.17312592369308705, 0.10194084070081284, -0.05089527633445614, 1.0072011228858682, -9.076750786677959, -1.9208117179560502, 3.6840925687938926]

931 2
[0.6675274841739512, 0.15475704287276543, 0.08759890938472249, 0.004778257977331357, 0.7488917497109605, 0.13559348201579802, 0.08903628672260418, -0.04267917150328537, 0.9378228035900843, -8.928447870733255, -3.956970899094931, 2.015934771194261]

932 1
[0.7253121482794819, 0.1298296527826325, -0.005155364297129178, 0.020324994063082115, 0.8505445417780245, -0.0010676309119750163, 0.1396037562023148, -0.027783008486056582, 0.9101230979884315, -9.157886997647058, -2.348809511697728, 3.180328559900424]

932 2
[0.7002111932365511, 0.13390213426262626, -0.001107648807348427, 0.04939221758992899, 0.8420392693383956, -0.00532508269105385, 0.15404879428074464, -0.03030819453911683, 0.9099876503565075, -9.202441796199492, -1.6985867808341801, 3.462606830334276]

933 1
[0.739241980122163, 0.14358005174882987, 0.004706493914730672, -0.017163608672111426, 0.8171216562023826, 0.07263994885342802, 0.06462148445174695, -0.0003743416304194565, 0.9747841274457753, -8.7146147660107, -3.0338076432062913, 3.805442122431372]

933 2
[0.7027229966590727, 0.1909215904645016, 0.0029090178595882754, 0.02121096635041835, 0.758953242578327, 0.09804670100294859, 0.06891778631564359, -0.017661793977819546, 0.9749761327642605, -9.194106478652156, -2.14051918048588, 3.2424340418373334]

934 1
[0.7203491733952768, 0.1271395035672391, 0.06693393412852683, 0.04797986856693853, 0.7391615014016452, 0.10275207094113384, 0.00900258030525878, 0.01191411252663397, 1.0033233435173934, -9.192727037914526, -2.1326330196162218, 3.2678119264330365]

934 2
[0.6704739402463737, 0.17216685208504126, 0.11599261398479287, 0.08917865972164403, 0.6313859834340313, 0.08008332838188018, 0.005262831227515525, 0.01545422589011691, 0.9910281651841416, -8.459187626470682, -3.5395624754394075, 3.956300032227807]

935 1
[0.6990872893169091, 0.17338534756680318, -0.009017626495641612, -0.03328683187685223, 0.8221573025029433, 0.06406330091462932, 0.07181947152565583, 0.027249193695014214, 0.9388971372279198, -8.94280218386031, -2.9976796818612472, 3.260914487769583]

935 2
[0.6737884902994964, 0.15510626317983586, 0.020268182143893823, -0.00013702752734052166, 0.8191816213247942, 0.02931860016580245, 0.06639575322057026, 0.03890958935465381, 0.9269827030128591, -9.028674331989738, -2.7438142405102024, 3.2600897489610583]

936 1
[0.6845309414464298, 0.23489448380323102, -0.05648194821150479, -0.07409354108892807, 0.8299673958174831, 0.10548529448053671, 0.19988902446293522, -0.08406174694498764, 0.9521267499692682, -8.475940899203332, -2.8629025122949012, 4.416251720725391]

936 2
[0.7195544816176092, 0.14598643769213301, 0.0024598441184789654, 0.015674445038611585, 0.8702585911115847, -0.0004508378840718209, 0.11405350176069715, -0.02052295575435408, 0.9469952889826796, -8.637744612423615, -2.822315192134971, 4.102263379392144]

937 1
[0.7433285563785172, 0.10505238557085463, 0.022267902419673843, -0.02102392320531291, 0.8483541025986773, 0.021374355993727667, 0.07312116200097689, 0.01889139388153349, 0.9297778422166155, -8.443608683428183, -4.304485583975168, 3.1294835228553572]

937 2
[0.7354496596088189, 0.12164832518954316, 0.028394333652222506, -0.0013182418593048958, 0.8479233525205723, 0.04275074258531867, 0.061712584560227726, 0.024928441989241393, 0.9335053608733676, -8.82138210347987, -3.1339388819430987, 3.4540402930635112]

938 1
[0.7320796629122449, 0.12000942750002667, 0.02439916841871466, 0.037192067376422555, 0.795199656964074, 0.09299011296726242, 0.09360276414123415, 0.003955123843954397, 0.9287411169941153, -9.396935832732785, -1.0312303083374217, 3.205116613515503]

938 2
[0.7177158216529806, 0.1400215046908216, 0.05891830634347151, 0.08232000776105462, 0.6884096923726557, 0.046920228229969066, 0.033965287754071226, 0.05614733939071523, 0.9266668448031569, -8.810886392869833, -2.9673689754381716, 3.608537070674284]

939 1
[0.677257493777049, 0.1406388142807914, 0.02276676073507629, 0.07733026582827124, 0.8099592567444045, 0.009212397656195888, 0.08970497743199418, 0.03858318117095848, 0.9034441584423238, -9.095861136297051, -1.9333600768626664, 3.6179806311024874]

939 2
[0.6751761324282666, 0.1622010329331639, 0.04453829243364664, 0.09492214884615631, 0.8084176928078164, 0.010607949168679754, 0.08129960850255737, 0.03760338075003738, 0.895115965477865, -9.194730910717128, -2.0077721132276114, 3.305756028350391]

940 1
[0.675582634439194, 0.20295496042008204, -0.030230145868141323, -0.05598811902084475, 0.8058563235853657, 0.10346468159020669, 0.13944366707546138, -0.026773145298923787, 0.9202258949265614, -9.289060739799183, -2.872161617282886, 2.207832580798058]

940 2
[0.6676151369561875, 0.22578704875013209, -0.03221710217028445, -0.05899936915052837, 0.8035618817789391, 0.11004618544837788, 0.15494402231694623, -0.030560487466609217, 0.9189497264604534, -8.831529854002769, -2.942993173936643, 3.584695457310042]

941 1
[0.7340405246865556, 0.0966393834334839, 0.0319058689006336, 0.02309686199225411, 0.8761822030319022, 0.010421435822520073, 0.08697584169258028, 0.04722768322152219, 0.8872272620498898, -9.368246622586799, -1.3283980685358026, 3.1879451911190637]

941 2
[0.7039168114488961, 0.10572511774903598, 0.031054130049777265, 0.01530696463800541, 0.8588441785267222, -0.028553844627740164, 0.07944375284835459, 0.06622542981460619, 0.8753323677742223, -9.20992586052637, -2.421033442173801, 2.9837938077758364]

942 1
[0.775890954613191, 0.0628154086317032, 0.058014884690983363, 0.09473239266216421, 0.794894755512951, 0.03317551872991685, 0.05741323110229021, 0.04674707764237856, 0.9182234143065425, -9.417297007659771, -0.9794461533690783, 3.1690220936277473]

942 2
[0.737700988189779, 0.009750237083128924, 0.10968131131543267, 0.07788562358851266, 0.8576486509381537, -0.02383378045798267, 0.05550195129481595, 0.0511959267203556, 0.9024655009687194, -9.11675001579172, -1.791123432832731, 3.6544544705594815]

943 1
[0.6919877913787291, 0.17281233409755697, 0.001543025859742142, -0.017917213111234724, 0.7668833925442713, 0.11999315364839233, 0.13871370758061752, -0.015228660894442678, 0.9139585256703695, -8.986818553381992, -2.0074161086166704, 3.84652412027328]

943 2
[0.6619070556231619, 0.182004438991383, 0.015917911177423194, -0.019271222806551032, 0.7462741204912644, 0.10623379412258668, 0.14972378305830672, -0.013392114139582323, 0.9041790796089478, -8.846168127929312, -2.6001948580466494, 3.8142829846086044]

944 1
[0.7105229380197478, 0.16635121649434084, 0.008272657718990578, 0.04022779995015242, 0.7400116529245895, 0.06878768907104399, 0.06222523710053587, 0.06178849569254391, 0.8977509998171627, -8.847967383810792, -2.868835033326941, 3.5705049095912873]

944 2
[0.6711323498165404, 0.17304743754161386, 0.04805126193431017, 0.08401791807964726, 0.6833672766983432, 0.03865288082722439, 0.04884290595232444, 0.06759658166178163, 0.891016749941326, -8.867574904252741, -3.2732082513141227, 3.1619277157634262]

945 1
[0.747438609224109, 0.09095081342961385, 0.04042582848208748, 0.006160337166904263, 0.8960273503909019, 0.00338378235317148, 0.055371097394844714, 0.047335830041862106, 0.9060636056085244, -9.34660189028761, -2.113036468124529, 2.7849514532801467]

945 2
[0.7312885367731516, 0.08224800245857927, 0.046536699887894606, 0.009201616355408417, 0.8850609628077766, -0.016285743894023208, 0.03931561757491824, 0.08681428207022061, 0.8986930170000488, -8.930506522560764, -2.522273613757453, 3.676929170209869]

946 1
[0.7262770979369138, 0.09800008049690179, 0.05637787150007793, 0.01601302913808797, 0.8149874619489232, 0.02423563185056514, 0.04501547227766211, 0.04626724672548083, 0.923855310594537, -8.84033777259203, -3.291589732165433, 3.2678539889003817]

946 2
[0.7132224104604431, 0.13241391616476922, 0.0302967517913372, 0.045121318955220535, 0.7570413573223181, 0.04545536572503929, 0.05743945021504341, 0.06735331733682912, 0.9154423340399164, -8.740119612583795, -2.2339134250524024, 4.284622574853127]

947 1
[0.7064539669049896, 0.1029148108204741, 0.048052721835640966, -0.009131228504057136, 0.8405904491410444, 0.044481419801357905, 0.06035398211285323, 0.008646669393413476, 0.9614901047214315, -9.212042539353881, -2.8190366780345855, 2.6034662760016607]

947 2
[0.7225029167755113, 0.12053596976449191, 0.05378201363437286, 0.02709181222135996, 0.8287308446194757, 0.046151539499138444, 0.04354901624403322, 0.007357425388378287, 0.9776774827925432, -8.88208477067388, -2.6501873766160875, 3.7091012049312417]

948 1
[0.7126892563318401, 0.17607446318770703, -0.008135101443486642, 0.008778235406376156, 0.8014496278737187, 0.0924288785544155, 0.12593578830533594, -0.041602997046802856, 0.9498224632250083, -8.933148507379336, -2.8311719656737346, 3.4085330402964895]

948 2
[0.6890208638458436, 0.15736489356196304, 0.0333473707842509, 0.042031900694030476, 0.7909376592929914, 0.034955811162133574, 0.10406853095243175, -0.007840564547739324, 0.951123429318397, -8.109008527368337, -3.4894562607055444, 4.645718866849171]

949 1
[0.7595840563110862, 0.06159499644775055, 0.05756052610343234, 0.059065614484595526, 0.789133175099305, 0.01782668759355408, 0.002569130421413258, 0.07694611714537596, 0.9377327730147217, -9.099141913143006, -2.554319421104402, 3.1998274824257735]

949 2
[0.7083280035181327, 0.09253729127336355, 0.059472298006287054, 0.030251863150906003, 0.8265864845195218, 0.03890907538106875, 0.01781215417092077, 0.06545116038190928, 0.928519752295813, -9.31480106218804, -2.4215039381953325, 2.6465001970379523]

950 1
[0.7447337343981554, 0.0671149973661476, 0.0626346636427191, 0.08596871116017203, 0.7775250692324369, 0.039447586297362974, 0.04608148139162358, 0.03481811415592236, 0.9388982381647649, -9.231913080100714, -1.422532670678435, 3.528816651111998]

950 2
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951 1
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951 2
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952 1
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952 2
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953 1
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953 2
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954 1
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954 2
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955 1
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955 2
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956 1
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956 2
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957 1
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957 2
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958 1
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958 2
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959 1
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959 2
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960 1
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960 2
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961 1
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961 2
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962 1
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962 2
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963 1
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963 2
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964 1
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964 2
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965 1
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965 2
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966 1
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966 2
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967 1
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967 2
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968 1
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968 2
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969 1
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969 2
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970 1
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970 2
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971 1
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971 2
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972 1
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972 2
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973 1
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973 2
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974 1
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974 2
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975 1
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975 2
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976 1
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976 2
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977 1
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977 2
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978 1
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978 2
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979 1
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979 2
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980 1
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980 2
[0.6806261142978458, 0.1582970655780866, 0.05018171216494163, 0.049884822533974714, 0.8087570162049726, 0.013372627249716261, 0.11133476500696585, 0.011078699807179026, 0.8874081530931983, -9.043557007546926, -2.5132493920870886, 3.3676384701732096]

981 1
[0.7618295096175883, 0.017955891674229826, 0.08353623430893461, 0.08099856874138711, 0.8451737637879372, -0.02817101372121731, 0.026795264077009415, 0.07176910571818096, 0.9189574335403613, -9.239232863424, -1.6696782727253707, 3.408451053745427]

981 2
[0.745685018047091, 0.0867801696853968, 0.0502868090451462, 0.09273915630642673, 0.762619743687078, -0.0008424694632874208, 0.039719709493526094, 0.0664287518766531, 0.9171850157747123, -9.191930759783608, -1.6638050029208267, 3.541304188980627]

Discriminability

Raw BNU1:

Affined BNU1:

Raw BNU2:

Affined BNU2:

We can clearly see that all the results have been consistent with our expectations. Our understanding appears to be appropriate. I expect the algorithm to perform similarly well on other real datasets. Not only is it widely used in the fMRI analysis community, but we have also seen that it met are qualitative and quantitative expectations. We could perhaps improve performance further by diving deeper into some of the more specific options that are available in the tool, once we have a better understanding of our datasets themselves.